Magnetic resonance imaging (MRI) is widely used in clinical imaging. However, the presence of metallic implants can either preclude the use of MRI due to safety or limit the diagnostic quality of MRI due to induced variation in the static magnetic field. Distortion-free imaging near MRI-safe devices has been enabled by 3D multispectral imaging (MSI) techniques, including slice encoding for metal artifact correction (SEMAC), multi-acquisition variable-resonance image combination (MAVRIC) [1], and the MAVRIC-SEMAC hybrid, MAVRIC-SL [2].
However, the additional dimensions required to resolve slice distortion induced by off-resonance resulting from metal lead to long scan times and reduced signal-to-noise ratio (SNR), even when distortions only comprise a small fraction of the image. For an image consisting only of on-resonance, slice phase encoding is redundant, motivating the need for a method that exploits this redundancy to reduce acquisition requirements.
Constrained image models have enabled accelerated imaging with improved SNR, coverage, and resolution by exploiting a priori information about images and image formation. Parallel imaging, partial Fourier imaging, and compressed sensing are widely used examples that have been applied to 3D MSI. Some work using compressed sensing to exploit the sparse support of slice profiles has shown potential (approximately twofold acceleration) [3, 4, 5]. The spatial support structure of excited slices can also be used as a constraint, and this approach offers up to twofold acceleration [6]. However, these methods do not exploit dependencies between bins. One approach is to explicitly represent the relationship between the image (magnetization) and signal parameters (magnetic field) in image reconstruction [7], but accurate modeling of image formation and the required nonlinear optimization are both challenging. Another approach is to use a generalized sensivity encoding framework, which also requires a calibration [8].
To our knowledge, little work has been done to exploit the redundancy of slice phase encoding for the on-resonance signal and spatial distribution of off-resonance. The spatial distribution of off-resonance bins in MAVRIC using a novel calibration procedure across bins was previously introduced, but its efficacy in combination with parallel imaging was not demonstrated and its physical basis and potential beyond MAVRIC was not demonstrated [9]. In SEMAC and the hybrid method, the spatial extent of off-resonance has been constrained on a bin-by-bin basis [10], but the model is limited by the spatial support of the excited slice.